epa_viz_code

Below, I walk through a number of predictors in the model, holding others steady, to get an impression of how each impacts the model predictions.

PLAY-LEVEL FACTORS

Game Time and Field Position

Figure 1. Heatmap

Figure 2. Surface Plot

Figure 3. Line plot

Cursory Observation

• Generally, expected points don’t drop off precipitously until very late in the half
• Expected points for the road team remain fairly stable over the majority of the half, but are higher and decline slightly over time for the home team

Down

Figure 4. Dumbbell plot

Cursory Observation

• Down-to-down differences in expected points are fairly stable no matter where on the field you compare

Timeouts

Figure 5. Heatmap

Cursory Observation

• Fewer timeouts for the defense earlier increases expected points for the office earlier in the game, but in late game situations, it reduces expected points scored by the office
• An offense with few timeouts late in the game is expected to score more
• Timeouts are probably a stand-in for game script, with teams using timeouts being a sign they’re actively trying to score, especially at the end of the game if they’re still in contention

GAME-LEVEL FACTORS

Eras

Figure 6. Line plot

Cursory Observation

• Older seasons are predicted to have slightly lower EP
• Season differences in EP are more pronounced for away team possessions

Stadium Roof

Figure 7. Lollipop plot

Cursory Observation

• Outdoor games reduce EP for the possessing team, dome games increase them